Image data processing method by reducing image noise, and camera integrating means for implementing said method

ABSTRACT

The invention concerns image data processing, through noise reduction comprising the following steps: associating a learning zone (ZA) with a reference point (Pref) of the image (IM); for each variable point (PC, PC′,) of the learning zone, evaluating a distance (d, d′,) between: values of points in a first window (f 1 ) of the image, centered on the reference point, and values of points in a second window (f 2 , f′ 2 ,), of similar format as the format of the first window and centered on the variable point; repeating said distance calculation for all the points of the learning zone as successive variable points and estimating an average value to assign to the reference point, said average being weighted on the basis of the distances evaluated for each variable point.

The present invention relates to the processing of image data byreducing noise.

It may be applied to photography for the public at large, to digitalvideo, to medical imaging, or to any new image acquisition system. It isalso indicated that the invention may be applied, advantageously to therestoration of films.

Especially with the success of digital cameras with the public at large,the need to restore digital images taken en masse under oftenunfavorable conditions has recently appeared. However, these images mayexhibit noise, be it due to the unfavorable picture-taking conditions orto digital restoration operations.

The existing, known, methods of restoration are not industriallyapplicable because they depend on parameter settings requiring veryaccurate knowledge of the nature of the noise, in particular. Moreover,these methods all introduce unacceptable degradations, called artifacts,such as blur, oscillations, stair casing, losses of detail and textures.This is why, at the moment, a high signal-to-noise ratio, typicallyequivalent to a factor of 100 at least, is necessary in digital cameras.Under-exposed images have a low signal-to-noise ratio and cannot berestored with currently known techniques.

Once digitized, an image takes the form of a generally rectangular arrayof values. In the case of a time sequence of images, such as a film,there is a series of such arrays, over time.

The association of a point in the array and of the gray level (for ablack and white image) or of color levels (typically red, blue and greenfor a color image), at this point is called:

-   -   a “pixel” for a bidimensional image, and    -   a “voxel” for a tridimensional image (in particular in medical        imaging).

We shall also speak of a “temporal pixel” for a point belonging to asequence of images over time, for example, a point which evolves fromone image to another in a film. It will then be understood that a timecoordinate, additional with respect to a conventional pixel, will thenbe associated with a “temporal pixel”. In the case of a monodimensionalimage, that is to say a signal, one speaks of a sample for theassociation of a point and of its value.

Each point is the result of a measurement, generally made by a matrix ora strip of light sensors such as CCDs (standing for “Couple ChargeDevices”). A point corresponds to a small square of the CCD matrix,within which the number of photons arriving is counted. The arrival ofthe photons follows a random process introducing fluctuations about amean value. Moreover, each sensor may itself produce “dark noise” whichgets superimposed on the photon count.

Most images therefore contain noise, which is a random perturbation ofthe value of the point. Stated otherwise, the image observed, denoted I,follows a relation of the type I=I0+b, where I0 is the ideal image, withno noise, and b the noise.

The images obtained by other methods, like photosensitive paperprinting, also exhibit noise due to the chemical characteristics of thebacking used. This noise is of course retained during the digitizationof the photograph (or “scanning”). The printing of films for camerasalso leads to the appearance of small blotches that are also callednoise, here.

The noise may depend, at each point on the gray level of this point.Thus, there is in general more noise in the bright parts of the image,even if the signal-to-noise ratio is better there.

We also define what is understood by “signal/noise ratio”, here. Thisparameter designates the proportion of a gray level or color level whichmay originate from noise. For example, a signal/noise ratio of 100corresponds to fluctuations of a hundredth of the “true value” of thegray level. For standard digital images, the gray level or color levelis between 0 (black) and 255 (white). Noise becomes sensible once itexceeds a mean amplitude, or standard deviation, of 4 or 5, thiscorresponding to a signal/noise ratio of about 50. It is very useful tobe able to eliminate all kinds of noise from images, since noise is ahindrance to the viewing of the images and masks some of the details.Typically, an image with no noise appears sharper.

The size of the CCD sensors is, itself, dictated in part by therequirement for noise reduction. If one knew how to remove noiseeffectively, it would be possible to construct smaller sensors byapplying noise reduction processing to the signals sensed. It would thusbe possible to construct smaller cameras, with fewer CCD sensors, butproviding the same number of pixels as the existing cameras. It wouldalso be possible to design cameras with the same characteristics, butwith higher resolution or else identical cameras but usable with ashorter exposure time.

Finally, many restoration operations on digital images amplify the noiseand therefore require to be coupled with de-noising processing. Theoperations increasing the contrast of under-exposed images amplify thenoise. Likewise, the operations removing blur from an image contributeto an increase in the noise.

Unfortunately, the known de-noising algorithms tend to confuse the noisewith small details of the image. For example, the noise reductionmethods described in particular in documents U.S. Pat. No. 6,681,054 andU.S. Pat. No. 6,539,125 use a weighting based on the spatial distancesbetween a point to be processed and current points surrounding thispoint to be processed, to calculate a mean to be assigned to the valuesof the point to be processed. Consequently, if locally the imageexhibits a great variety of shades, after noise reduction processingbased on a weighting with respect to a spatial distance, this variety ofshades is no longer to be found.

Such methods destroy the image in part. The gain in image quality istherefore doubtful.

The present invention comes to improve the situation.

For this purpose it proposes a method of processing image data, byreducing image noise, comprising the following steps:

a) obtaining points of the image with respective values associated withthe points,

b) associating a learning zone with a reference point of the image, and

c) and assigning the reference point new values obtained by a weightedstatistical estimation, of weighted mean or weighted median type usingthe values of the points included in the learning zone.

The method within the meaning of the invention comprises more preciselythe following steps:

c1) for each current point of the learning zone, evaluating a distancecharacterizing a resemblance between

-   -   the values of the points in a first window of the image,        centered on the reference point, on the one hand and    -   the values of the points in a second window, of the same format        as the first window and centered on the current point, on the        other hand,

c2) and repeating step c1) for all the points of the learning zone inthe guise of successive current points by using the distances obtainedfor all the current points to calculate the weights used in the weightedstatistical estimation of step c).

Preferably, step c) is applied to all the points of the image in theguise of successive reference points, so as to globally process theentire image.

It is also indicated that the learning zone may correspond to the wholeof the image. However, in an advantageous variant, the learning zonebelongs to one or more model images, different from the image to beprocessed.

As indicated previously, the points may be pixels for a bidimensionalimage, voxels for a tridimensional image, or else temporal pixels whenthe image to be processed is a film. Finally, for a signal representinga monodimensional image such as a film with a single pixel per image,these points are ultimately successive samples.

As indicated hereinabove, it is now possible to redefine the size oflight sensors of a camera or else their exposure time to light, byapplying the noise reduction processing within the meaning of theinvention.

Thus, in step a) of the method within the meaning of the invention, thepoints of the image are acquired from one or more light sensors of givenarea, imposing a predetermined exposure time to light, per unit area, onthis or these sensors. It is indicated that, in a general manner, adecrease in the exposure time brings about an increase in the noise.

Advantageously, if the implementation of steps c1), c2) and c) offers areduction in the noise by a factor K, a reduction, substantially by afactor K², in the exposure times of the sensor or sensors is authorizedso as to operate at substantially constant signal-to-noise ratio.

In a configuration where one operates at constant signal-to-noise ratio,and at constant exposure duration, the number of sensors per unit areamay advantageously be increased, so as to increase, substantially by afactor K², the resolution of the image acquired and processed.

In this regard, the present invention is also aimed at a camera equippedwith one or more sensors and comprising means of control of the exposuretime of the sensors for the implementation of the method hereinabove.There is advantageously provision to equip this camera with a processingunit, adequately programmed to apply the method of processing within themeaning of the invention to the signals acquired by the sensor orsensors of the camera. More particularly, this processing unit comprisesa memory able to store a computer program product comprisinginstructions for the implementation of all or part of the steps of themethod hereinabove.

In this regard, the present invention is also aimed at such a computerprogram product, intended to be stored in a memory of a processing unitof the aforesaid type, or else on a removable memory medium, such as aCD-ROM or a diskette, intended to cooperate with a reader of theprocessing unit.

Other advantages and characteristics of the invention will appear onreading the detailed description hereinafter, given by way ofnonlimiting example, and on examining the drawings in which:

FIG. 1 illustrates very diagrammatically the elements of a cameraentering into the implementation of the processing within the meaning ofthe invention,

FIG. 2 diagrammatically represents the format of the windows to becompared for the evaluation of the distance and, thereby, of theaforesaid weighting,

FIG. 3 diagrammatically represents a succession of windows of respectiveincreasing sizes serving for the evaluation of the aforesaid distance,in an embodiment described hereinafter,

FIG. 4 summarizes the main steps of the method according to a preferredembodiment, and

FIG. 5 represents additional steps of the method hereinabove, in apreferred embodiment.

Referring first of all to FIG. 1, the camera defined hereinabovecomprises a matrix of CCD sensors receiving a luminous flux emanatingfrom an object OBJ, the two elements being for example housed in a blackbox (not represented). It also comprises an acquisition card 1, inparticular for converting the signals emanating from the CCD sensorsinto digital samples, typically pixels, which are stored temporarily ina work memory 2 so as to be processed by a processor 3.

It is recalled that a digital image is made up of pixels which may belikened to points on a grid, furnished with a gray value or with colorlevels.

A memory 4, for example a read-only memory, stores the instructions of acomputer program product for the implementation of the method accordingto the invention. The pixels processed may, thereafter, be transmittedby an interface 5 which, in the example described, is a graphicsinterface for the visualization of the image processed by display screenECR. However, it is indicated that, as a variant, the interface 5 may bea communication interface for transmitting the processed pixels to aremote entity. It may also be an interface to a storage unit, forexample for storage on a memory medium, so as to subsequently recoverthe pixels acquired and processed by the method within the meaning ofthe invention. Finally, it may be an interface to a unit for printing onpaper.

In yet another variant, the samples acquired are transmitted directly toa remote entity comprising a processing unit for applying the noisereduction process within the meaning of the invention to these samples,rather than providing for the processing of the samples at the cameraitself.

Hereinafter, the principles of the method within the meaning of theinvention are described in broad outline before describing a preferredembodiment thereof.

Referring to FIG. 2, we consider a digital image IM and a pixel Pref.Represented in FIG. 2 is a bidimensional image. One therefore speakshereinafter of pixel to designate a point of the image. However, asindicated hereinabove, the image may be tridimensional and comprisevoxels. The image may also be a film and comprise temporal pixels,without these various distinctions affecting the principles of theinvention, as will be seen.

I(Pref) designates the value of the color (or of the gray level for ablack and white image) associated with the pixel Pref. This value iseither an integer or real number (for a gray levels image) or a triplein the case of color images in the RGB standard (standing forRed-Green-Blue), or else possible an n-tuple in the case of amultispectral image, again without these various distinctions affectingthe principles of the invention.

For a degraded image IM, the objective is to suppress the degradationswhile preserving the principal characteristics of the image, as well asthe details of small dimensions and the textures. To achieve thisobjective, the processing within the meaning of the invention does notmake any hypothesis regarding the nature of the noise, nor of the image.A knowledge of the type of noise will, however, make it possible tospecify and adapt several elements of the method such as the type ofstatistical calculation to be performed (weighted mean or weightedmedian, for example), or else the size to be fixed for the resemblancewindows. The processing is nevertheless based on the fact that all thestandard images exhibit a high degree of redundancy. It is thenconsidered that around each pixel there are pixels which resemble itvery greatly.

The processing may therefore be described in very general terms asfollows:

-   -   for each pixel p, of value I(p), we search for the pixels of the        image which resemble it most (these pixels which resemble it are        denoted p_1, . . . , p_n),    -   the initial value I(p) is thereafter replaced by a mean of the        values of I(p_1), . . . , I(p_n).

This mean operation reduces the noise. However, the quality of thisprocessing depends on its capacity to find the pixels which mostresemble a given pixel.

Most of the known techniques of “de-noising” proceed thus. Typically,those described in documents U.S. Pat. No. 6,681,054 and U.S. Pat. No.6,539,125, cited hereinabove, replace each value I(p) by a weighted meanof the values of the closest pixels, in terms of spatial distance,assuming that the closest pixels are also the ones that resemble itmost.

In the processing within the meaning of the present invention, therequirement of resemblance is favored relative to the criterion ofspatial proximity.

Referring to FIG. 2, there are represented a plurality of points PC,PC′, . . . belonging to a learning zone ZA which may be situated in theimage to be processed IM or sometimes more advantageously, in otherimages considered as models. Also considered is a reference point Prefto be processed and belonging, for its part, necessarily to the imageIM. We construct windows of the same format f1, f2, f′2, . . .respectively about points Pref, PC, PC′, . . . . The respectivedistances d, d′, etc. between the windows f1 and f2, and then betweenthe windows f1 and f′2, etc. are evaluated. In the example representedin FIG. 2, the window f2 including the point PC comprises image patternsMOA in the form of oblique stripes, while the windows f1 and f′2respectively including the reference point Pref and the other currentpoint PC′ are both “white”. In this case the processing within themeaning of the present invention will allocate a larger distance d tothe window f2 than the distance d′ which is allocated to the window f′2,even if, nevertheless, the point PC is closer, in distance, to thereference point Pref than the point PC′.

It is indicated that a resemblance between two pixels within the meaningof the present invention, is evaluated as possible:

-   -   consider two pixels Pref and PC, as represented in FIG. 2,    -   we consider a first “resemblance window” f1, for example square,        centered on the pixel Pref, and we consider a second window f2        of the same shape, but centered on the pixel PC,    -   the pixels Pref and PC are deemed to resemble one another if the        windows f1 and f2 resemble one another (the resemblance is        measured with respect to a norm which will be specified as a        preferred example later).

The set of pixels which are a predetermined distance from the centralpixel is called “window centered at a pixel”. It is indicated that thesewindows are preferably square, here. However, their shape may also berectangular, oval or other.

It is indicated also that, in a general manner, a criterion of distancebetween windows is established, this distance being evaluated forexample on the basis of a weighted sum of squares of the differences ofvalues of points, between the first window and the second window. Anyother distance, norm or measure evaluating the resemblance of the twowindows is also conceivable.

The resemblance between two windows may be preferably estimated as afunction of the sum of the squares of the differences of the gray levelsor color levels at each window pixel, as will be seen in detail later.

It will be noted that there is no need to establish a threshold betweenwindows which resemble one another and windows which do not resemble oneanother. For each resemblance window, a weight is evaluated which variesinversely with the distance d. The weighted mean making it possible tocalculate the value assigned to the restored pixel of I(Pref) istherefore a mean where the windows which resemble one another count alot, while the windows which do not resemble one another count forlittle or not at all.

Typically, in order for the noise to be divided by a factor of two, itsuffices for the number of windows that truly resemble one another toexceed 4, this being practically always the case. The errors introducedby the customary techniques are due particularly to the fact that theresemblance of the pixels whose mean is evaluated is not a parameterthat is taken fully into account.

FIG. 4 summarizes these steps. A reference pixel Pref, to be processed,in the image, is designated in step 41. With it is associated a windowof predetermined dimension f1, in step 42. It is indicated that in step43, the designation of the reference pixel to be processed also makes itpossible to associate therewith a learning zone ZA and to designate,accordingly the current pixels PC, PC′, . . . , belonging to thislearning zone ZA and on the basis of which the calculation of the meanwill be undertaken.

In step 44, the method continues by constructing second windows f2, f′2,. . . associated with the current pixels PC, PC′, etc. In step 45,respective distances d, d′, etc. are evaluated according to a criterionof resemblance, between the window f1 and the window f2, between thewindow f1 and the window f′2, etc.

In step 46, a weighted mean MOY is evaluated as a function of the valuesof pixels val(PC), val(PC′), . . . of the current points PC, PC′, . . ., as well as of the respective distances d, d′, . . . calculated in step45. Typically:MOY=val(PC)/D+val(PC′)/D′+ . . .where D, D′, . . . are values which vary like the distances d, d′, . . ..

In step 47, the value MOY is assigned to the reference pixel Pref andthis new value val(Pref) is preferably stored in memory for laterdisplay of the image, or other (step 50). In step 48, a test verifieswhether there is still another pixel of the image to be processed, inwhich case this new pixel is designated in the guise of reference pixelin step 41, which is implemented again with the following steps 42 to50. Otherwise the method stops at step 49.

Specified hereinbelow are the calculations performed at certain steps ofthe method of FIG. 4, in the guise of exemplary embodiment andnevertheless making it possible to achieve processing efficacysufficient for the reduction of the noise by a factor 2.

With each pixel p is associated a “learning zone centered at p”, ZA,defined as a window A(p, N) of any shape (square, circular, or other),centered at 0 and of fixed size (2N+1)×(2N+1).

The new value I_(res)(p) associated with p may be written as a weightedmean of the values I(q) of the pixels q which belong to the learningzone A(p,N), according to a relation of the type:

$\begin{matrix}{{I_{res}(p)} = {\sum\limits_{q \in {A{({p,N})}}}{{w\left( {p,q} \right)}{I(q)}}}} & (1)\end{matrix}$where the weights w(p,q) vary inversely with the distance between thepixels p and q, according to a criterion of resemblance betweenassociated respective windows.

The family of the weights of a learning zone associated with a pixel pis such that:

${0 \leq {2{w\left( {p,q} \right)}} \leq {1\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{q \in {A{({p,N})}}}{w\left( {p,q} \right)}}}} = 1$

To evaluate the similarity between the pixels, we firstly define a“resemblance window centered at the pixel p”, denoted W(p,M), centeredabout the pixel p and of fixed size (2M+1)×(2M+1).

The resemblance between pixels will depend on the resemblance of colorvalues (or of gray levels) between the windows W(p,M) and W(q,M). In themean expressed in relation (1), the weight w(p,q) is all the larger themore the pixels in W(p,M) and W(q,M) resemble one another. Typically, inthe example represented in FIG. 2, the weight W(Pref, PC′) is largesince the resemblance between the windows f1 and f′2 includingrespectively Pref and PC′ is large. On the other hand, the weightw(Pref, PC) is small since the resemblance windows are different, thewindow f2 including stripes as indicated hereinabove.

To calculate the resemblance between the windows, we firstly define thedistance between W(p,i) and W(q,i) as the Euclidian norm of a differencevector, according to a relation of the type:

$\begin{matrix}{{{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}} = {\frac{1}{{2i} + 1}\sqrt{\sum\limits_{j}\left\lbrack {{{W\left( {p,i} \right)}(j)} - {{W\left( {q,i} \right)}(j)}} \right\rbrack^{2}}}},} & (2)\end{matrix}$where

-   -   W(p,i)(j) are the (2i+1)² values of the points of the window of        size (2i+1)×(2i+1) and centered on the point p, and    -   W(q,i) (j) are the (2i+1)² values of the points of the window of        size (2i+1)×(2i+1) and centered on the point q.

Two windows W(p,i) and W(q,i) are said to be “similar” if the norm givenabove is small.

However, in a preferred embodiment, with reference to FIG. 3, twowindows f1 and f2 of the same size (2M+1)×(2M+1) are denoted W(p,M) andW(q,M) are similar if and only if so are their subwindows W(p,i) andW(q,i) of size (2i+1)×(2i+1), with 1≦i≦M.

Referring to FIG. 3, the window f1, centered on the reference pointPref, as in FIG. 2, includes a plurality of subwindows (W(p,1), W(p,2),W(p,3), . . . , W(p,i), . . . , W(p,M)) nested within one another,centered on the point Pref and of respective increasing sizes (3×3, 5×5,7×7, . . . , (2i+1)×(2i+1), . . . , (2M+1)×(2M+1), from W(p,1) toW(p,M).

Likewise, the window f2, centered on the current point PC, as in FIG. 2,includes a plurality of subwindows (W(q,1), W(q,2), W(q,3), . . . ,W(q,i), . . . , W(q,M)) nested within one another, centered on thecurrent point PC and of increasing respective sizes (3×3, 5×5, 7×7, . .. , (2i+1)×(2i+1), . . . , (2M+1)×(2M+1)), from W(q,1) to W(q,M). Thewindows f1 and f2 like the subwindows W(p,i) and W(q,i) (for all the ifrom 1 to M), are of the same format and, in particular, of the samerespective sizes.

We then define the distance between the windows f1 and f2 as a normreferred to here as “generalized” of the difference vector, according toa relation of the type:

$\begin{matrix}{{{{d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}}}}},{with}}{{{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}} = {\frac{1}{{2i} + 1}\sqrt{\sum\limits_{j}\left\lbrack {{{W\left( {p,i} \right)}(j)} - {{W\left( {q,i} \right)}(j)}} \right\rbrack^{2}}}},}} & (3)\end{matrix}$as indicated hereinabove.

It is recalled that M is an integer such that (2M+1)×(2M+1) is the size,in number of points, of the windows f1 and f2, denoted W(p,M) andW(q,M).

However, it is indicated that, for a color image in the RGB standard,the aforesaid distance may be evaluated on the basis of the relation:

${{d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}}}}},{{but}\mspace{14mu}{with}}$${{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}} = {\frac{1}{{2i} + 1}\sqrt{\sum\limits_{j}{\sum\limits_{u}\left\lbrack {{{W_{u}\left( {p,i} \right)}(j)} - {{W_{u}\left( {q,i} \right)}(j)}} \right\rbrack^{2}}}}},$where:

-   -   W_(u)(p,i)(j) are the (2i+1)² vector values with coordinates u        of the points of a current sub-window, of size (2i+1)×(2i+1)        included in the first window f1 and centered on the reference        point p, and    -   Wu(q,i)(j) are the (2i+1)² vector values with coordinates u of        the points of a current sub-window, of size (2i+1)×(2i+1),        included in the second window f2 and centered on the current        point q.

These coordinates u are then respective levels of blue, of red and ofgreen.

In a general manner, the weighting assigned to a current point qdecreases with the distance between the values associated with thispoint q and the values associated with the reference point p.

The weight w(p, q) representing the weighting assigned to a currentpoint q for the estimation of the mean value which will be assigned tothe reference point q, is then defined according to a relation of thetype:

$\begin{matrix}{{w\left( {p,q} \right)} = {\frac{1}{Z(p)}{\exp\left( {{- {d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack}}/h} \right)}}} & (4)\end{matrix}$where

-   -   A(p,N) is the learning zone centered on the reference point p        and of size (2N+1)×(2N+1), and    -   h is a parameter indicative of a degree of filtering of the        image.

It is specified here that Z(p) is a constant such that the sum of theweights w(p,q) is equal to 1. Z(p) is therefore defined simply on thebasis of the relation:

$\begin{matrix}{{Z(p)} = {\sum\limits_{q \in {A{({p,N})}}}{\exp\left( {{{- {d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack}}/h},} \right)}}} & (5)\end{matrix}$

This method therefore involves three parameters, that can be fixed forthe digital images adapted to applications for the public at large asfollows.

-   -   The first parameter is the size of the learning zone        (2N+1)×(2N+1). This zone must be large enough to make it        possible to take account of the statistical properties of the        image. Trials have shown that the size of the learning zone, in        number of points, is preferably greater than 20×20. Typically, a        zone of 21×21 pixels is sufficient to guarantee suitable        learning.    -   The second parameter is the size of the resemblance window        (2M+1)×(2M+1). Too small a window may lead to too local a        comparison. On the other hand, too large a window may have the        consequence that no window is truly similar. Here again, trials        have shown that the size of the first f1 and second f2 windows        is greater than 10×10 and preferably, greater than or equal to        15×15. Typically, the size of resemblance windows f1 and f2 of        15×15 is optimal for Gaussian noise of standard deviation 20. A        size of window of 11×11 already provides very good results. This        size is indicated for an image with gray levels. For a color        image, the size of the resemblance window may be reduced to 7×7        and for weak noise down to 3×3.    -   The third parameter is the parameter h which indicates the        degree of filtering of the image. This parameter is preferably        fixed at a value of around 3. However, this value may be chosen        smaller if the image is hardly degraded.

It may, however, be that other parameter sets are more judicious formore targeted applications, such as medical imaging or the restorationof films.

The method applied to a bidimensional image with the values of N, M andh hereinabove makes it possible to increase the signal/noise ratio by aratio greater than 2. This measure may be verified by taking a not verynoisy image, and thereafter adding artificial noise to it. Theperformance of the processing is measured exactly by comparing the meanrelative error of the image restored with the noise-free original, aswell as the relative error introduced by the noise. We then routinelynote a multiplication by a factor of more than 2 of the signal/noiseratio. Thus, for example, CCD sensors that are four times smaller (2²)can be used without increasing noise, as indicated hereinabove. It isindicated elsewhere that the computer program product within the meaningof the invention can be stored in a memory of a photographic developmentapparatus, of a digital camera, or of a device for restoring digitalimages, but the application of the invention may also influence thedesign of the sensors of any new camera or new photographic apparatus bypermitting a signal/noise ratio reduced by a factor of larger than four.

However, in a sophisticated embodiment, it is preferred to calculate thestandard deviation of the noise present in an image and to fix theoptimal size (2M+1)×(2M+1) of the resemblance windows accordingly. Thelatter may range from M=1 to M=7, in an image to be processed and towhich a prior zoom has been applied, as will be seen later withreference to a preferred embodiment. The size in pixels of theresemblance window therefore varies between 3×3 and 15×15 in the zoomedimage. The 3×3 size in a color image with noise with standard deviationof less than or around 13 makes it possible to restore the finestdetails.

Referring to FIG. 5, the method within the meaning of the invention, orin an advantageous embodiment, comprises the following global steps,after obtaining the raw image IM in step 51:

-   -   in step 52, the image to be processed IM is magnified by a        digital zoom operation,    -   the processing TR within the meaning of the invention and        corresponding preferably to steps 41 to 50 of FIG. 4 is applied,        and    -   in step 55, the image processed I_(res) is reduced in the        inverse sense by a zoom-out operation, so as to restore the        processed image I_(res).

Prior to the processing step proper TR, it is also advantageous, asindicated hereinabove, to

-   -   estimate a level of noise Ib in the image to be processed (step        53), and    -   adapt the format M, in particular in terms of number of points,        of the resemblance windows as a function of the estimated noise        level (step 54).

It has turned out in fact that the processing was more efficacious if itis applied after a zoom in accordance with Shannon's sampling theory(zoom by FFT). The position of the resemblance windows becoming moreaccurate, the restoration of the fine textures is improved.

The processing is more efficacious also if the value of the restoredpixels depends not only on the mean of the values of the resemblancewindows, but also on the variance of these values, that can be estimatedby a method known per se.

Thus, the processing by noise reduction within the meaning of theinvention consists globally in replacing the value at each pixel of theimage by a weighted mean of all the values of the pixels of the image.The weighting is done in such a way that a window which greatlyresembles the window centered at the pixel contributes greatly to themean, whilst a window that does not resemble it very much hardlycontributes thereto. The resemblance between two windows of the sameformat is evaluated on the basis of a function of the differences ofvalues between respective pixels of the two windows, for example aquadratic mean of these differences, or any other norm, deviation ordistance measuring the resemblance of the two windows more finely.

This weighted mean, on account of the redundancies inherent in theimages, confirms a good value while reducing the error due to noise by afactor of greater than 2. The method within the meaning of the inventiondoes not presuppose any prior knowledge about the noise or the image.

This method makes it possible to use sensors with internal noise orphoton noise that is more than four times greater and to eliminate thenoise created by various restoration operations (typically deblurring,extension of the gray scale for under-exposed photographs, or others).

Of course, the present invention is not limited to the embodimentdescribed hereinabove by way of example; it extends to other variants.

In a more general manner, it is indicated that the weighted means may bereplaced by other statistical estimators such as median or weightedmedian according to the type of noise envisaged. It is also indicatedthat the calculation of the weights in the weighted mean or the weightedmedian may depend, apart from on the distance based on a resemblancecriterion as described hereinabove on other statistical parametersestimated globally in the image and on each resemblance window, such as,for example the variance of each window or the estimated variance of thenoise in the image.

Again, in a general manner, the method described hereinabove is suitedto an image of arbitrary dimension, either color or black and white, toa film or to a tridimensional image, irrespective of its origin (oncellulose or digital). If the image is initially in nondigital form itis scanned beforehand.

1. A method of processing image data, by reducing image noise,comprising the following steps: a) obtaining points of the image withrespective values associated with the points, b) associating a learningzone, containing a plurality of points, with a reference point of theimage, c) and assigning the reference point new values obtained by aweighted statistical estimation, of weighted mean or weighted mediantype using the values of the points included in the learning zone, themethod characterized in that it comprises the following steps: c1) foreach current point of the learning zone, valuating a distance between awindow around the reference point and a window around the current pointbased on a resemblance between the values of the points in the referencepoint window, centered on the reference point, on the one hand and thevalues of the points in the current point window, of the same format asthe reference point window and centered on the current point, on theother hand, c2) and repeating step c1) for all the points of thelearning zone in the guise of successive current points by using thedistances obtained for all the current points to calculate the weightsused in the weighted statistical estimation of step c) wherein saiddistance is evaluated from the relation:${{d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}}}}},\;{with}$${{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}} = {\frac{1}{{2i} + 1}\sqrt{\sum\limits_{j}{\sum\limits_{u}\left\lbrack {{{W_{u}\left( {p,i} \right)}(j)} - {{W_{u}\left( {q,i} \right)}(j)}} \right\rbrack^{2}}}}},$wherein M is an integer such that (2M+1)×(2M+1) is the size, in numberof points, of the reference point window, W_(u)(p,i)(j) are the (2i+1)²vector values with coordinates u of the points of a current sub-window,of size (2i+1)×(2i+1) included in the reference point window andcentered on the reference point p, and W_(u)(q,i)(j) are the (2i+1)²vector values with coordinates u of the points of a current sub-windowof size (2i+1)×(2i+1), included in the current point window and centeredon the current point q, and wherein the steps are carried out by aprocessing unit.
 2. The method as claimed in claim 1, characterized inthat step c) is applied to all the points of the image in the guise ofsuccessive reference points.
 3. The method as claimed in claim 1,characterized in that the learning zone corresponds to the whole of theimage.
 4. The method as claimed in claim 1, characterized in that thelearning zone belongs to one or more model images, different from theimage to be processed.
 5. The method as claimed in claim 1,characterized in that the points are pixels for a bidimensional image orvoxels for a tridimensional image, or else temporal pixels when theimage to be processed is a film.
 6. The method as claimed in claim 1,characterized in that said distance is evaluated on the basis of a sumof squares of the differences of values of points, between the referencepoint window and the current point window.
 7. The method as claimed inclaim 1, in which the image is a color image, characterized in that saidcoordinates u designate positions of respective levels of blue, of redand of green.
 8. The method as claimed in claim 1, characterized in thatthe weighting assigned to a current point q decreases with the distancebetween the values associated with this point q and the valuesassociated with the reference point p.
 9. The method as claimed in claim8, characterized in that the weighting assigned to a current point q isgiven by the relation:${{w\left( {p,q} \right)} = {\frac{1}{Z(p)} = {\exp\left( {{- {d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack}}/h} \right)}}},{{with}\text{:}}$${{Z(p)} = {\sum\limits_{q \in {A{({p,N})}}}{\exp\left( {{- {d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack}}/h} \right)}}},$where: A(p,N) is the learning zone centered on the reference point p andof size (2N+1)×(2N+1), h is a parameter indicative of a degree offiltering of the image.
 10. The method as claimed in claim 1,characterized in that the size of the learning zone, in number ofpoints, is greater than 20×20.
 11. The method as claimed in claim 10,characterized in that the size of the reference point window and currentpoint window is greater than 10×10 and, preferably, greater than orequal to 15×15 and, in a color image, preferably greater than or equalto 5×5, or even 3×3 for the images which are not very noisy.
 12. Themethod as claimed in claim 10, characterized in that the parameter h hasa value of around
 3. 13. The method as claimed in claim 1, in which, instep a) the points of the image are acquired from one or more lightsensors of given area, by imposing on the sensor(s) a predetermined timeof exposure to light, per unit area, a decrease in the exposure timebringing about an increase in the noise, characterized in that, theimplementation of steps c1), c2) and c) offering a reduction in thenoise by a factor K, a reduction, substantially by a factor K², in theexposure times of the sensor or sensors is authorized so as to operateat substantially constant signal-to-noise ratio.
 14. The method asclaimed in claim 13, characterized in that one operates at constantsignal-to-noise ratio, and at constant exposure duration, whilstincreasing the number of sensors per unit area, so as to increase,substantially by a factor K², the resolution of the image acquired andprocessed.
 15. The method as claimed in claim 1, characterized in thatit comprises prior steps comprising estimating a level of noise in theimage to be processed, and adapting the format, in particular in termsof 10 number of points, of said reference point window and current pointwindow as a function of the estimated noise level.
 16. The method asclaimed in claim 1, characterized in that it comprises the followingglobal steps: magnifying the image to be processed by a digital zoomoperation, applying steps a), b), c1), c2) and c), and reducing in thereverse sense the image processed, by a zoom-out operation.
 17. Aprocessing unit comprising a non-transitory computer readable medium,storing a program, which when executed causes the processing unit tocarry out each step as recited in claim
 1. 18. A camera equipped withmultiple image sensors and a processor that controls exposure timings ofthe sensors, wherein the processor reduces image noise by employing themethod of claim
 1. 19. A method of processing image data, by reducingimage noise, comprising the following steps: a) obtaining points of theimage with respective values associated with the points, b) associatinga learning zone, containing a plurality of points, with a referencepoint of the image, c) and assigning the reference point new valuesobtained by a weighted statistical estimation, of weighted mean orweighted median type using the values of the points included in thelearning zone, the method characterized in that it comprises thefollowing steps: c1) for each current point of the learning zone,valuating a distance between a window around the reference point and awindow around the current point based on a resemblance between thevalues of the points in the reference point window, centered on thereference point, on the one hand and the values of the points in thecurrent point window, of the same format as the reference point windowand centered on the current point, on the other hand, c2) and repeatingstep c1) for all the points of the learning zone in the guise ofsuccessive current points by using the distances obtained for all thecurrent points to calculate the weights used in the weighted statisticalestimation of step c), wherein said distance is evaluated from therelation:${{d\left\lbrack {{W\left( {p,M} \right)};{W\left( {q,M} \right)}} \right\rbrack} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}}}}},{with}$${{{{W\left( {p,i} \right)} - {W\left( {q,i} \right)}}} = {\frac{1}{{2i} + 1}\sqrt{\sum\limits_{j}\left\lbrack {{{W\left( {p,i} \right)}(j)} - {{W\left( {q,i} \right)}(j)}} \right\rbrack^{2}}}},$wherein M is an integer such that (2M+1)×(2M+1) is the size, in numberof points, of the reference point window, W(p,i)(j) are the (2i+1)²values of the points of a current sub-window, of size (2i+1)×(2i+1)included in the reference point window and centered on the referencepoint p, and W(q,i)(j) are the (2i+1)² values of the points of a currentsub-window, of size (2i+1)×(2i+1), included in the current point windowand centered on the current point q, and wherein the steps are carriedout by a processing unit.